An Compression Technology for Effective Data on Cloud Platform

  • Youchan ZhuEmail author
  • Li Zhou


There are more and more application systems running on the cloud platform, which will produce large amounts of effective data everyday. In order to preserve them and make fun use of the storage space, those effective data must be compressed and those compressed data, if necessary, should be recovered correctly. Meanwhile, there are a lot of equivalent data item values (or equivalent data item values within the system error) in the original data. So, it is not right to compress those effective data directly. In order to make fun use of the storage space and correctly recover the original data, a new method occurs. When compressed, those effective data must be processed firstly and then the handled data should be compressed with Huffman coding; when the compressed data need recover, the process is against with that of data compression.The experiment shows that this method has the advantages of fast compression speed, high compression ratio and lossless recovery .


Cloud platform Block Classification Reorganization Huffman coding 


  1. 1.
    J. Duda, K. Tahboub, N. J. Gadgil and E. J. Delp, The use of asymmetric numeral systems as an accurate replacement for Huffman coding[J], Picture Coding Symposium, pp. 65–69, 2015.Google Scholar
  2. 2.
    W. Wang and W. Zhang, Adaptive spatial modulation using Huffman coding[J], IEEE Global Communications Conference (GLOBECOM), pp. 1–6, 2016.Google Scholar
  3. 3.
    A. M. Rufai, G. Anbarjafari and H. Demirel, Lossy medical image compression using Huffman coding and singular value decomposition[J], Signal Processing and Communications Applications Conference (SIU), pp. 1–4, 2013.Google Scholar
  4. 4.
    H. Abid and S. Qaisar, Distributed video coding for wireless visual sensor networks using low power Huffman coding[J], 44th Annual Conference on Information Sciences and Systems (CISS 2010), pp. 1–6, 2010.Google Scholar
  5. 5.
    W. Wei, Y. K. Liu, X. D. Duan and C. Guo, Improved compression vertex chain code based on Huffman coding[J], Journal of Computer Applications, Vol. 12, pp. 3565–3569, 2014.Google Scholar
  6. 6.
    E. H. Yang and C. Sun, Dithered soft decision quantization for baseline JPEG encoding and its joint optimization with Huffman coding and quantization table selection[J], Asilomar Conference on Signals, Systems and Computers, pp. 249–253, 2011.Google Scholar
  7. 7.
    K. S. Kasmeera, S. P. James and K. Sreekumar, Efficient compression of secured images using subservient data and Huffman coding[J], Procedia Technology, Vol. 25, pp. 60–67, 2016.CrossRefGoogle Scholar
  8. 8.
    W. Wang and W. Zhang, Huffman coding based adaptive spatial modulation[J], IEEE Transactions on Wireless Communications, Vol. PP, No. 99, pp. 1–1, 2017.Google Scholar
  9. 9.
    S. J. Yun, M. R. Usman, M. A. Usman and S. Y. Shin, Swapped Huffman tree coding application for low-power wide-area network (LPWAN)[J], IEEE International Conference on Smart Green Technology in Electrical and Information Systems, pp. 53–58, 2016, DOI: 1109/ICSGTEIS.2016.7885766.Google Scholar
  10. 10.
    R. Arshad, A. Saleem and D. Khan, Performance comparison of Huffman Coding and Double Huffman Coding[J], Sixth International Conference on Innovative Computing Technology, pp. 361–364, 2016.Google Scholar
  11. 11.
    A. Vaish and M. Kumar, A new Image compression technique using principal component analysis and Huffman coding[J], International Conference on Parallel, Distributed and Grid Computing (PDGC), pp. 301–305, 2014.Google Scholar
  12. 12.
    M. Nelon and J. -L. Gaily, The Data Compression Book, 2nd ed. MIS Press, 1995.Google Scholar
  13. 13.
    J. Radhakrishnan, S. Sarayu, K. George Kurian, D. Alluri and R. Gandhiraj, Huffman coding and decoding using android[J], International Conference on Communication and Signal Processing (ICCSP), pp. 0361–0365, 2016.Google Scholar
  14. 14.
    R. Patel, V. Kumar, A. Tyagi and V. Asthana, A fast and improved image compression technique using Huffman coding[J], International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), pp. 2283–2286, 2016.Google Scholar
  15. 15.
    K. S. Venkata, K. T. K. C. Rhishi, B. Karthikeyan, V. Vaithiyanathan and R. M. M. Anishin, A hybrid technique for quadrant based data hiding using Huffman coding[J], International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), pp. 1–6, 2015.Google Scholar
  16. 16.
    X. K. Liu, K. Chen and B. Li, Huffman coding and applications in compression for vector maps[J], Applied Mechanics & Materials, Vol. 333–335, pp. 718–722, 2014.Google Scholar
  17. 17.
    C. C. Chang, T. S. Nguyen and C. C. Lin, A novel compression scheme based on SMVQ and Huffman coding[J], International Journal of Innovative Computing, Information & Control, Vol. 10, No. 3, pp. 1041–1050, 2013.Google Scholar
  18. 18.
    L. -C. Petrini and V. -M. Ionescu, Implementation of the Huffman coding algorithm in windows 10 IoT core[J], International Conference on Electronics, Computers and Artificial Intelligence (ECAI), Vol. 8, pp. 1–6, 2016.Google Scholar
  19. 19.
    T. Kumaki, Y. Kuroda and T. Koide, CAM-based VLSI architecture for Huffman coding with real-time optimization of the code word table [image coding example][J], IEEE International Symposium on Circuits & Systems, Vol. 5, pp. 202–5205, 2005.Google Scholar
  20. 20.
    J. Wu, Y. Wang and L. Ding, Improving performance of network covert timing channel through Huffman coding[J], Mathematical and Computer Modelling, Vol. 55, No. 1–2, pp. 69–79, 2012.MathSciNetCrossRefzbMATHGoogle Scholar
  21. 21.
    Y. H. Lee, D. S. Kim and K. K. Hong, Class-dependent and differential Huffman coding of compressed feature parameters for distributed speech recognition[J], IEEE International Conference on Acoustics, pp. 4165–4168, 2009.Google Scholar
  22. 22.
    D. S. Kim and K. K. Hong, Voicing class dependent Huffman coding of compressed front-end feature vector for distributed speech recognition[J], Second International Conference on Future Generation Communication and Networking Symposia (FGCNS), Vol. 3, pp. 51–54, 2008.Google Scholar
  23. 23.
    J. H. Pujar and L. M. Kadlaskar, A new lossless method of image compression and decompression using huffman coding techniques[J], Journal of Theoretical and Applied Information Technology, Vol. 46, No. 1, pp. 11–16, 2012.Google Scholar
  24. 24.
    A. Vaish and M. Kumar, A new image compression technique using principal component analysis and Huffman coding[J], International Conference on Parallel, Distributed and Grid Computing (PDGC), Vol. 1, pp. 301–305, 2014.Google Scholar
  25. 25.
    J. H. Jiang, S. C. Shie and W. D. Chung, A reversible image steganographic scheme based on SMVQ and Huffman coding[J], International Conference on Connected Vehicles and Expo (ICCVE), pp. 486–487, 2013.CrossRefGoogle Scholar
  26. 26.
    A. Kawabata, T. Koide and H. J. Mattausch, Optimization vector quantization by adaptive associative-memory-based codebook learning in combination with Huffman coding[J], Proceedings 2010 First International Conference on Networking and Computing (ICNC 2010), pp. 15–19, 2010.Google Scholar
  27. 27.
    J. Duda, K. Tahboub, N. J. Gadgil and E. J. Delp, The use of asymmetric numeral systems as an accurate replacement for Huffman coding[J], Picture Coding Symposium, pp. 65–69, 2015.Google Scholar
  28. 28.
    M. Hameed, Low power text compression for Huffman coding using altera FPGA with power management controller[J], 1st International Scientific Conference of Engineering Sciences–3rd Scientific Conference of Engineering Science (ISCES), pp. 18–23, 2018.Google Scholar
  29. 29.
    G. C. Chang and Y. D. Lin, An efficient lossless ECG compression method using delta coding and optimal selective Huffman coding[J], In Conjunction with 14th International Conference on Biomedical Engineering (ICBME) and 5th Asia Pacific Conference on Biomechanics (APBiomech), Vol. 31, pp. 1327–1330, 2010.Google Scholar
  30. 30.
    R. P. Jasmi, B. Perumal and M. P. Rajasekaran, Comparison of image compression techniques using Huffman coding, DWT and fractal algorithm[J], International Conference on Computer Communication and Informatics (ICCCI), pp. 1–5, 2015.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Control and Computer EngineeringNorth China Electric Power UniversityBaodingChina

Personalised recommendations